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Welcome to Day 4 of our AI Testing Myths vs. Realities Challenge! Today, we’re diving deep into the world of data.

Our topic of the day revolves around how AI generates realistic test scenarios. In the realm of software testing, realistic data is key to ensuring accurate results and robust performance.

AI excels at creating these scenarios, simulating a wide range of real-world conditions to thoroughly test and validate systems.

We want to hear from you! Share your insights and experiences on how AI achieves this feat. Join the conversation and tell us which one you think is the reality and which is the myth and explain your reasoning in the comments below for a chance to win a ShiftSync gift box!

Stay engaged as we continue to explore and demystify the world of AI testing.

Click here to check the rest of the questions.

Tune in tomorrow for the Final Day 5 Challenge.

Here Understanding business logic is still a myth, and test data generation is a reality.

AI can understand business logic, but not to the extent humans can. There are some limitations on the way they can ingest and process the data. Prompting matters, and we need to train the AI like we train newly hired colleagues in our company. That’s why I considered it a myth, and I believe very soon that this myth is going to become reality.

 

I had been a part of one synthetic test data generator where I initially started with generating some data for the address fields, and it worked well. Finally, with the correct prompt and combining the Python capabilities, by using AI, we have cracked the best synthetic data generator ever. And also, we already know Gen AI is very good at generating things, and generating test data is its cup of tea.

 

Happy learning!


AI can generate a lot of test data that is without a doubt true. As you as you give it the right format and the right prompt you can expect to get test data in minutes. For those that have not yet used it to generate test data I can assure you it is way more than just a lorem ipsum generator.

 

Now the topic of business logic that is still a myth. Because for AI to be able to generate specific data that relates to business logic you would need an AI trained on your code, on your database structure and on your data. And even then I am not really sure it would be able to generate correct data.

 

Any oder ideas, opinions?


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